SoroushVahidi/scidocs huggingface dataset:
SPROUT: Lazy vs. Eager Query Plans for Tuple-Independent Probabilistic Databases
(2009)

A paramount challenge in probabilistic databases is the scalable computation of confidences of tuples in query results. This paper introduces an efficient secondary-storage operator for exact computation of queries on tuple-independent probabilistic databases. We consider the conjunctive queries without self-joins that are known to be tractable on any tuple-independent database, and queries that are not tractable in general but become tractable on probabilistic databases restricted by functional dependencies. Our operator is semantically equivalent to a sequence of aggregations and can be naturally integrated into existing relational query plans. As a proof of concept, we developed an extension of the PostgreSQL 8.3.3 query engine called SPROUT. We study optimizations that push or pull our operator or parts thereof past joins. The operator employs static information, such as the query structure and functional dependencies, to decide which constituent aggregations can be evaluated together in one scan and how many scans are needed for the overall confidence computation task. A case study on the TPC-H benchmark reveals that most TPC-H queries obtained by removing aggregations can be evaluated efficiently using our operator. Experimental evaluation on probabilistic TPC-H data shows substantial efficiency improvements when compared to the state of the art.
R-ADMAD: high reliability provision for large-scale de-duplication archival storage systems
(2009)

Data de-duplication has become a commodity component in data-intensive systems and it is required that these systems provide high reliability comparable to others. Unfortunately, by storing duplicate data chunks just once, de-duped system improves storage utilization at cost of error resilience or reliability. In this paper, R-ADMAD, a high reliability provision mechanism is proposed. It packs variable-length data chunks into fixed sized objects, and exploits ECC codes to encode the objects and distributes them among the storage nodes in a redundancy group, which is dynamically generated according to current status and actual failure domains. Upon failures, R-ADMAD proposes a distributed and dynamic recovery process. Experimental results show that R-ADMAD can provide the same storage utilization as RAID-like schemes, but comparable reliability to replication based schemes with much more redundancy. The average recovery time of R-ADMAD based configurations is about 2-6 times less than RAID-like schemes. Moreover, R-ADMAD can provide dynamic load balancing even without the involvement of the overloaded storage nodes.
Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning
(2017)

Model selection is one of the most computationally expensive tasks in a machine learning application. When dealing with kernel methods for structures, the choice with the largest impact on the overall performance is the selection of the feature bias, i.e. the choice of the concrete kernel for structures. Each kernel in turn exposes several hyper-parameters which also need to be fine tuned. Multiple Kernel Learning offers a way to approach this computational bottleneck by generating a combination of different kernels under different parametric settings. However, this solution still requires the computation of many large kernel matrices. In this paper we propose a method to efficiently select a small number of kernels on a subset of the original data, gaining a dramatic reduction in the runtime without a significant loss of predictive performance.
Deep Kernelized Autoencoders
(2017)

In this paper we introduce the deep kernelized autoencoder, a neural network model that allows an explicit approximation of (i) the mapping from an input space to an arbitrary, user-specified kernel space and (ii) the back-projection from such a kernel space to input space. The proposed method is based on traditional autoencoders and is trained through a new unsupervised loss function. During training, we optimize both the reconstruction accuracy of input samples and the alignment between a kernel matrix given as prior and the inner products of the hidden representations computed by the autoencoder. Kernel alignment provides control over the hidden representation learned by the autoencoder. Experiments have been performed to evaluate both reconstruction and kernel alignment performance. Additionally, we applied our method to emulate kPCA on a denoising task obtaining promising results.
An Innovative Approach to Investigate Various Software Testing Techniques and Strategies
(2016)

Software testing is a way of finding errors from the system. It helps us to identify and debug mistakes, errors, faults and failures of a system. There are many techniques and strategies emerged since the concept of software development emerged. The aim of testing is to make the quality of software as efficient as possible.in this paper we discuss most widely used techniques and strategies. Where they can be used and how they can be used. How they work and how they differ (from each other).They are the following. Techniques: Black Box Testing, White Box Testing, And Grey Box Testing. Strategies: Unit Testing, System Testing, And Acceptance Testing.
Online Dominant and Anomalous Behavior Detection in Videos
(2013)

We present a novel approach for video parsing and simultaneous online learning of dominant and anomalous behaviors in surveillance videos. Dominant behaviors are those occurring frequently in videos and hence, usually do not attract much attention. They can be characterized by different complexities in space and time, ranging from a scene background to human activities. In contrast, an anomalous behavior is defined as having a low likelihood of occurrence. We do not employ any models of the entities in the scene in order to detect these two kinds of behaviors. In this paper, video events are learnt at each pixel without supervision using densely constructed spatio-temporal video volumes. Furthermore, the volumes are organized into large contextual graphs. These compositions are employed to construct a hierarchical codebook model for the dominant behaviors. By decomposing spatio-temporal contextual information into unique spatial and temporal contexts, the proposed framework learns the models of the dominant spatial and temporal events. Thus, it is ultimately capable of simultaneously modeling high-level behaviors as well as low-level spatial, temporal and spatio-temporal pixel level changes.
Intelligent Condition Based Monitoring Using Acoustic Signals for Air Compressors
(2016)

Intelligent fault diagnosis of machines for early recognition of faults saves industry from heavy losses occurring due to machine breakdowns. This paper proposes a process with a generic data mining model that can be used for developing acoustic signal-based fault diagnosis systems for reciprocating air compressors. The process includes details of data acquisition, sensitive position analysis for deciding suitable sensor locations, signal pre-processing, feature extraction, feature selection, and a classification approach. This process was validated by developing a real time fault diagnosis system on a reciprocating type air compressor having 8 designated states, including one healthy state, and 7 faulty states. The system was able to accurately detect all the faults by analyzing acoustic recordings taken from just a single position. Additionally, thorough analysis has been presented where performance of the system is compared while varying feature selection techniques, the number of selected features, and multiclass decomposition algorithms meant for binary classifiers.
Recurrent-OctoMap: Learning State-Based Map Refinement for Long-Term Semantic Mapping With 3-D-Lidar Data
(2018)

This letter presents a novel semantic mapping approach, Recurrent-OctoMap, learned from long-term three-dimensional (3-D) Lidar data. Most existing semantic mapping approaches focus on improving semantic understanding of single frames, rather than 3-D refinement of semantic maps (i.e. fusing semantic observations). The most widely used approach for the 3-D semantic map refinement is “Bayes update,” which fuses the consecutive predictive probabilities following a Markov-chain model. Instead, we propose a learning approach to fuse the semantic features, rather than simply fusing predictions from a classifier. In our approach, we represent and maintain our 3-D map as an OctoMap, and model each cell as a recurrent neural network, to obtain a Recurrent-OctoMap. In this case, the semantic mapping process can be formulated as a sequence-to-sequence encoding–decoding problem. Moreover, in order to extend the duration of observations in our Recurrent-OctoMap, we developed a robust 3-D localization and mapping system for successively mapping a dynamic environment using more than two weeks of data, and the system can be trained and deployed with arbitrary memory length. We validate our approach on the ETH long-term 3-D Lidar dataset. The experimental results show that our proposed approach outperforms the conventional “Bayes update” approach.
Learning to generate chairs with convolutional neural networks
(2015)

We train a generative convolutional neural network which is able to generate images of objects given object type, viewpoint, and color. We train the network in a supervised manner on a dataset of rendered 3D chair models. Our experiments show that the network does not merely learn all images by heart, but rather finds a meaningful representation of a 3D chair model allowing it to assess the similarity of different chairs, interpolate between given viewpoints to generate the missing ones, or invent new chair styles by interpolating between chairs from the training set. We show that the network can be used to find correspondences between different chairs from the dataset, outperforming existing approaches on this task.
Comics on the Brain: Structure and Meaning in Sequential Image Comprehension
(2011)

Just as syntax differentiates coherent sentences from scrambled word strings, sequential images must also use a cognitive system to distinguish coherent narratives from random strings of images. We conducted experiments analogous to two classic psycholinguistic studies to examine structure and semantics in sequential images. We compared Normal comic strips with both structure and meaning to sequences with Semantics Only, Structure Only, or randomly Scrambled panels. Experiment 1 used a target-monitoring paradigm, and found that RTs were slowest to target panels in Scrambled sequences, intermediate in Structural Only and Semantic Only sequences, and fastest in Normal sequences. Experiment 2 measured ERPs to the same strips. The largest N400 appeared in Scrambled and Structural Only sequences, intermediate in Semantic Only sequences and smallest in Normal sequences. Together, these findings suggest that sequential image comprehension is guided by an interaction between a structure and meaning, broadly analogous to syntax and semantics in language.
A comparative analysis of machine learning techniques for student retention management
(2010)

Student retention is an essential part of many enrollment management systems. It affects university rankings, school reputation, and financial wellbeing. Student retention has become one of the most important priorities for decision makers in higher education institutions. Improving student retention starts with a thorough understanding of the reasons behind the attrition. Such an understanding is the basis for accurately predicting at-risk students and appropriately intervening to retain them. In this study, using five years of institutional data along with several data mining techniques (both individuals as well as ensembles), we developed analytical models to predict and to explain the reasons behind freshmen student attrition. The comparative analyses results showed that the ensembles performed better than individual models, while the balanced dataset produced better prediction results than the unbalanced dataset. The sensitivity analysis of the Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.
A hybrid data analytic approach to predict college graduation status and its determinative factors
(2016)

Purpose – The prediction of graduation rates of college students has become increasingly important to colleges and universities across the USA and the world. Graduation rates, also referred to as completion rates, directly impact university rankings and represent a measurement of institutional performance and student success. In recent years, there has been a concerted effort by federal and state governments to increase the transparency and accountability of institutions, making “graduation rates” an important and challenging university goal. In line with this, the main purpose of this paper is to propose a hybrid data analytic approach which can be flexibly implemented not only in the USA but also at various colleges across the world which would help predict the graduation status of undergraduate students due to its generic nature. It is also aimed at providing a means of determining and ranking the critical factors of graduation status. Design/methodology/approach – This study focuses on developing a novel hybrid data analytic approach to predict the degree completion of undergraduate students at a four-year public university in the USA. Via the deployment of the proposed methodology, the data were analyzed using three popular data mining classifications methods (i.e. decision trees, artificial neural networks, and support vector machines) to develop predictive degree completion models. Finally, a sensitivity analysis is performed to identify the relative importance of each predictor factor driving the graduation. Findings – The sensitivity analysis of the most critical factors in predicting graduation rates is determined to be fall-term grade-point average, housing status (on campus or commuter), and which high school the student attended. The least influential factors of graduation status are ethnicity, whether or not a student had work study, and whether or not a student applied for financial aid. All three data analytic models yielded high accuracies ranging from 71.56 to 77.61 percent, which validates the proposed model. Originality/value – This study presents uniqueness in that it presents an unbiased means of determining the driving factors of college graduation status with a flexible and powerful hybrid methodology to be implemented at other similar decision-making settings.
Improved Use of Continuous Attributes in C4.5
(1996)

A reported weakness of C4.5 in domains with continuous attributes is addressed by modifying the formation and evaluation of tests on continuous attributes. An MDL-inspired penalty is applied to such tests, eliminating some of them from consideration and altering the relative desirability of all tests. Empirical trials show that the modi cations lead to smaller decision trees with higher predictive accuracies. Results also con rm that a new version of C4.5 incorporating these changes is superior to recent approaches that use global discretization and that construct small trees with multi-interval splits.
Feature Extraction with Convolutional Neural Networks for Handwritten Word Recognition
(2013)

In this paper, we show that learning features with convolutional neural networks is better than using hand-crafted features for handwritten word recognition. We consider two kinds of systems: a grapheme based segmentation and a sliding window segmentation. In both cases, the combination of a convolutional neural network with a HMM outperform a state-of-the art HMM system based on explicit feature extraction. The experiments are conducted on the Rimes database. The systems obtained with the two kinds of segmentation are complementary: when they are combined, they outperform the systems in isolation. The system based on grapheme segmentation yields lower recognition rate but is very fast, which is suitable for specific applications such as document classification.
Design and implementation of 0.7 to 7 GHz broadband double-ridged horn antenna
(2014)

In this paper, simulation and measurement results of 0.7-7 GHz double-ridged guide horn (DRGH) antenna with coaxial input feed section is presented. This antenna, due to the large frequency band required by standards, is appropriate to be used in electromagnetic compatibility (EMC) testing and antenna measurement as a transmitter. A step by step method for designing DRGH antenna is given. A suitable taper for the ridges in the horn is designed and its impedance variations along the horn are shown. In addition, a new structure for the electrical field probe in the feed section is introduced by which a shift-down of lower frequency to 0.7 GHz is achieved. A sensitivity analysis is done on the parameters of the proposed structure. Other parameters of the feed section are also investigated and optimized values are obtained. Finally, the proposed antenna has been fabricated and measurement results show good agreement with the simulation.
An empirical study of the mechanisms of mindfulness in a mindfulness-based stress reduction program.
(2009)

S. L. Shapiro and colleagues (2006) have described a testable theory of the mechanisms of mindfulness and how it affects positive change. They describe a model in which mindfulness training leads to a fundamental change in relationship to experience (reperceiving), which leads to changes in self-regulation, values clarification, cognitive and behavioral flexibility, and exposure. These four variables, in turn, result in salutogenic outcomes. Analyses of responses from participants in a mindfulness-based stress-reduction program did not support the mediating effect of changes in reperceiving on the relationship of mindfulness with those four variables. However, when mindfulness and reperceiving scores were combined, partial support was found for the mediating effect of the four variables on measures of psychological distress. Issues arising in attempts to test the proposed theory are discussed, including the description of the model variables and the challenges to their assessment.
Second screen applications and tablet users: constellation, awareness, experience, and interest
(2012)

This study investigates how tablet users incorporate multiple media in their television viewing experience. Three patterns are found: (a) only focusing on television, (b) confounding television viewing with other screen media (e.g. laptop, tablet) and (c) confounding television viewing with various media, including print and screen media. Furthermore, we question how the incorporation of screen media in this experience affects the practice of engaging in digital commentary on television content. Also, we inquire the uptake and interest in so-called 'second screen applications'. These applications allow extensions of the primary screen experience on secondary screens (e.g. tablet). The results, based on a sample of 260 tablet users, indicate that there is only a modest uptake and interest in using secondary screens to digitally share opinions. However, the use of second screen interaction with television content is not discarded: although there is still little awareness and experience, we notice a moderate interest in these apps.
Muscle simulation for facial animation in Kong: Skull Island
(2017)

For Kong: Skull Island, Industrial Light & Magic created an anatomically motivated facial simulation model for Kong that includes the facial skeleton and musculature. We applied a muscle simulation framework that allowed us to target facial shapes while maintaining desirable physical properties to ensure that the simulations stayed on-model. This allowed muscle simulations to be used as a powerful tool for adding physical detail to and improving the anatomical validity of both blendshapes and blendshape animations in order to achieve more realistic facial animation with less hand sculpting.
Autonomous semantic mapping for robots performing everyday manipulation tasks in kitchen environments
(2011)

In this work we report about our efforts to equip service robots with the capability to acquire 3D semantic maps. The robot autonomously explores indoor environments through the calculation of next best view poses, from which it assembles point clouds containing spatial and registered visual information. We apply various segmentation methods in order to generate initial hypotheses for furniture drawers and doors. The acquisition of the final semantic map makes use of the robot's proprioceptive capabilities and is carried out through the robot's interaction with the environment. We evaluated the proposed integrated approach in the real kitchen in our laboratory by measuring the quality of the generated map in terms of the map's applicability for the task at hand (e.g. resolving counter candidates by our knowledge processing system).
Conscious intention and brain activity
(2001)

The problem of free will lies at the heart of modern scientific studies of consciousness. An influential series of experiments by Libet has suggested that conscious intentions arise as a result of brain activity. This contrasts with traditional concepts of free will, in which the mind controls the body. A more recent study by Haggard and Eimer has further examined the relation between intention and brain processes, concluding that conscious awareness of intention is linked to the choice or selection of a specific action, and not to the earliest initiation of action processes. The exchange of views in this paper further explores the relation between conscious intention and brain activity.
Identifying emotional states using keystroke dynamics
(2011)

The ability to recognize emotions is an important part of building intelligent computers. Emotionally-aware systems would have a rich context from which to make appropriate decisions about how to interact with the user or adapt their system response. There are two main problems with current system approaches for identifying emotions that limit their applicability: they can be invasive and can require costly equipment. Our solution is to determine user emotion by analyzing the rhythm of their typing patterns on a standard keyboard. We conducted a field study where we collected participants' keystrokes and their emotional states via self-reports. From this data, we extracted keystroke features, and created classifiers for 15 emotional states. Our top results include 2-level classifiers for confidence, hesitance, nervousness, relaxation, sadness, and tiredness with accuracies ranging from 77 to 88%. In addition, we show promise for anger and excitement, with accuracies of 84%.
Efficient deep learning for stereo matching with larger image patches
(2017)

Stereo matching plays an important role in many applications, such as Advanced Driver Assistance Systems, 3D reconstruction, navigation, etc. However it is still an open problem with many difficult. Most difficult are often occlusions, object boundaries, and low or repetitive textures. In this paper, we propose a method for processing the stereo matching problem. We propose an efficient convolutional neural network to measure how likely the two patches matched or not and use the similarity as their stereo matching cost. Then the cost is refined by stereo methods, such as semiglobal maching, subpixel interpolation, median filter, etc. Our architecture uses large image patches which makes the results more robust to texture-less or repetitive textures areas. We experiment our approach on the KITTI2015 dataset which obtain an error rate of 4.42% and only needs 0.8 second for each image pairs.